Predictive Urban Growth Planning for a Mid-Size City Bhilwara Using Geospatial Analysis & AI-based Techniques

The rapid urbanization of Bhilwara City, Rajasthan, has posed serious difficulties for infrastructure, sustainable development & resource management . Traditional urban planning methods often fall short of anticipating future urban growth, leading to unplanned expansion and inefficient resource allocation. This research proposes a datacentric approach to predict and guide urban development in Bhilwara using advanced Geographic Information Systems (GIS), remote sensing, Artificial Intelligence (AI), and Machine Learning (ML) techniques. By integrating spatial remote sensing data and GIS with AI-driven predictive models, this study aims to simulate future growth patterns, assess infrastructure needs, and optimize resource distribution for Bhilwara as it evolves into a smart city. The research methodology involves collecting high-resolution spatial data through remote sensing, which will be processed and analyzed within a GIS framework. Machine learning algorithms will be employed to develop predictive models that simulate various growth scenarios, allowing for informed decision-making on infrastructure development and urban resource planning. The study gives policymakers a strong decision support tool to predict population increase, change in land use ,and infrastructure demands by visualizing these projections. This interdisciplinary approach ensures that the city's expansion is not only anticipated but strategically planned to enhance liveability, sustainability, and smart city initiatives. The findings of this research will contribute to more systematic, predictive approach to urban planning, ensuring that Bhilwara’s development is aligned with modern smart city standards while addressing the local context's unique challenges. Ultimately, this project’s goal is to fill the gap between data-driven forecasts and actual implementations of urban policies.